Sex with Support Vector Machines
Baback Moghaddam, Ming–Hsuan Yang · 2000
Nonlinear Support Vector Machines (SVMs) are investigated for visual sex classification with low resolution "thumbnail" faces (21by -12 pixels) processed from 1,755 images from the FERET face database. The performance of SVMs is shown to be superior to traditional pattern classifiers (Linear, Quadratic, Fisher Linear Discriminant, Nearest-Neighbor) as well as more modern techniques such as Radial Basis Function (RBF) classifiers and large ensembleRBF networks. Furthermore, the SVM performance (3.4% error) is currently the best result reported in the open literature.